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Glama
Enrique-S-J

sysops-mcp

by Enrique-S-J

get_cpu_status

Read-onlyIdempotent

Check CPU utilization, core counts, and load averages to identify saturation and performance issues.

Instructions

Get CPU utilization, core counts, and load averages.

Samples CPU for sample_seconds and reports utilization percentage, physical/logical core counts, 1/5/15-minute load averages, and load-per-core (values >= 1.0 indicate saturation).

Returns: str: Markdown summary or JSON object with keys: percent, sample_seconds, physical_cores, logical_cores, load_avg {1min,5min,15min}, load_per_core_1min, per_core_percent (optional).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
per_coreNoInclude per-core utilization breakdown.
sample_secondsNoHow long to sample CPU utilization (e.g. 1.0). Longer samples smooth spikes.
response_formatNo'markdown' for human-readable output, 'json' for structured data.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the bar is lower. The description adds valuable behavioral context beyond annotations: it samples for sample_seconds, reports load averages, and interprets load-per-core saturation. This meaningfully helps an agent understand what the tool actually does.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose, followed by sampling behavior and a compact return-value breakdown. Every sentence adds useful information, and the structured Returns block makes the output contract easy to parse without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only diagnostic tool with three optional parameters, annotations, and a documented return format, this description is complete. It states the output types, the keys returned, the optional per-core detail, and the saturation threshold. An agent has enough information to select and invoke the tool correctly without further inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds extra semantic value by explaining sample_seconds behavior and the load-per-core saturation threshold. It also clarifies the optional per_core_percent output, connecting the per_core parameter to its effect.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Get CPU utilization, core counts, and load averages.' This clearly differentiates it from sibling tools like get_memory_status and get_disk_usage. The return format section further confirms exactly what data the tool produces.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for CPU-focused monitoring but never explicitly says when to choose it over alternatives or when not to use it. It provides helpful interpretation guidance like 'values >= 1.0 indicate saturation,' but no direct comparison to sibling tools or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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